Remote photoplethysmography combining color channels with SNR maximization for respiratory rate assessment
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Author
Luguern, Duncan
Benezeth, Yannick
Moser, Virginie
Dunbar, Andrea
Braun, Fabian
Lemkaddem, Alia
Nakamura, Keisuke
Gomez, Randy
Dubois, Julien
Abstract
Monitoring of breathing rate considerably improves healthcare quality by providing information about the patient’s state. Yet, the current methods used to assess the respiratory rate have some drawbacks that could be improved. Recent developments of the non-contact based measurement of the respiratory rate brought the remote Photoplethysmography (rPPG) technique. This technique allows physiological parameters monitoring with a camera and ambient light. It measures the light absorption variation by the blood to extract several physiological parameters. The main issue with the rPPG method is that its signal quality is lower compared with the contact-based methods. Several ways to improve this quality may be explored to obtain a robust rPPG pipeline. One of these is the combination of the multidimensional temporal traces generated by a camera into a single temporal trace. The literature methods aimed to extract a signal with the maximal information using smart estimation of the combination coef?cients. These coef?cients are computed using statistical or physiological/optical properties of the traces. In this paper, we developed an algorithm that estimates a better respiratory signal than with regular methods. The algorithm named Energy Variance Maximization (EVM) estimates the linear combination that maximizes the Signal Noise Ratio (SNR) of the output trace. We compare our contribution with three state-of-the-art method: CHROM, PBV and PVM. The results obtained with this method are better than the state of the art methods, with the 1 rpm precision being 0.14 better and the Mean Absolute Error (MAE) being 0.78 rpm better than CHROM, the best of compared litterature methods.
Publication Reference
ISMICT 2020, Nara (Japan)
Year
2020